Deterioration diagnosis device, deterioration diagnosis system, deterioration diagnosis method, and storage medium for storing program

ABSTRACT

A deterioration diagnosis device including an acquisition unit that acquires sensing information including at least a captured image captured by an image capture device mounted on a moving body, driving condition information indicating driving details of the moving body, and position information corresponding to the captured image and the driving condition information; a deterioration degree analysis unit that analyzes a deterioration degree of an inspection target appearing in the captured image; and a priority ranking computation unit that computes a priority ranking of the inspection target based on deterioration degrees of the same inspection target appearing in multiple captured images identified by the position information, and the driving condition information corresponding to the identified inspection target.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of U.S. patentapplication Ser. No. 17/261,432 filed on Jan. 19, 2021, which is aNational Stage Entry of international application PCT/JP2019/026982filed on Jul. 8, 2019, which claims the benefit of priority fromJapanese Patent Application 2018-139617 filed on Jul. 25, 2018, thedisclosures of all of which are incorporated in their entirety byreference herein.

TECHNICAL FIELD

The present invention relates to a deterioration diagnosis device, adeterioration diagnosis system, a deterioration diagnosis method, and aprogram.

BACKGROUND ART

Public installations such as the road surfaces of roads and informationsigns installed on roadsides undergo deterioration over time.Administrative authorities diagnose the deterioration degree of publicinstallations that have deteriorated over time and perform maintenanceon the deteriorated public installations. A lot of labor is required forsuch diagnosis of the deterioration degree of public installations.

Patent Document 1 discloses technology for improving the diagnosticaccuracy of road states by using diagnosis information generated bymultiple vehicles.

Additionally, Patent Document 2 describes technology wherein videoimages taken by a visible-light camera mounted on a vehicle areimage-processed to measure the cracking rates of road surfaces, thepresence or absence of potholes in road surfaces, the road surfacemarking deterioration conditions, and the presence or absence ofdeterioration of road-associated installations such as guard rails andsigns. Said Patent Document 2 describes further including vehicleposition information, quantifying and compiling examination results,classifying road deterioration conditions, comparing the quantified roaddeterioration conditions with past damage data, and quantitativelypredicting future road deterioration tendencies.

CITATION LIST Patent Literature [Patent Document 1]

PCT International Publication No. WO 2018/025341

[Patent Document 2]

Japanese Unexamined Patent Application, First Publication No.2006-112127

SUMMARY OF THE INVENTION Problems to be Solved by the Invention

Improvements are required in the accuracy of work for identifying, fromamong the above-mentioned public installations including roads and otherassociated installations, inspection targets and inspection locationsthat are of high priority for maintaining a safe state within a limitedbudget. Even among the inspection targets and the inspection locationsthat are of high priority, it is desirable to begin repairs with publicinstallations that have a particularly high probability of posing ahazard to residents. However, under the current circumstances,identifying such hazardous sites necessitates human judgments such asinspections and resident complaints, and requires a lot of labor.

Therefore, the present invention has, for example, the purpose ofproviding a deterioration diagnosis device, a deterioration diagnosissystem, a deterioration diagnosis method, and a program that solve theabove-mentioned problem.

Means for Solving the Problems

According to a first aspect of the present invention, a deteriorationdiagnosis device is characterized by being provided with an acquisitionunit that acquires sensing information including at least a capturedimage captured by an image capture device mounted on a moving body,driving condition information indicating driving details of the movingbody, and position information corresponding to the captured image andthe driving condition information; a deterioration degree analysis unitthat analyzes a deterioration degree of an inspection target appearingin the captured image; and a priority ranking computation unit thatcomputes a priority ranking of the inspection target based ondeterioration degrees of the same inspection target appearing inmultiple captured images identified by the position information, and thedriving condition information corresponding to the identified inspectiontarget.

According to a second aspect of the present invention, a deteriorationdiagnosis system is characterized by being provided with an imagecapture device that captures an image of the outside of a moving body;an acquisition unit that acquires sensing information including at leasta captured image captured by the image capture device, driving conditioninformation indicating driving details of the moving body, and positioninformation corresponding to the captured image and the drivingcondition information; a deterioration degree analysis unit thatanalyzes a deterioration degree of an inspection target appearing in thecaptured image; and a priority ranking computation unit that computes apriority ranking of the inspection target based on deterioration degreesof the same inspection target appearing in multiple captured imagesidentified by the position information, and the driving conditioninformation corresponding to the identified inspection target.

According to a third aspect of the present invention, a deteriorationdiagnosis method is characterized by including steps of acquiringsensing information including at least a captured image captured by animage capture device mounted on a moving body, driving conditioninformation indicating driving details of the moving body, and positioninformation corresponding to the captured image and the drivingcondition information; analyzing a deterioration degree of an inspectiontarget appearing in the captured image; and computing a priority rankingof the inspection target based on deterioration degrees of the sameinspection target appearing in multiple captured images identified bythe position information, and the driving condition informationcorresponding to the identified inspection target.

According to a fourth aspect of the present invention, a program storedin a storage medium is characterized by making a computer in adeterioration diagnosis device execute a process of acquiring sensinginformation including at least a captured image captured by an imagecapture device mounted on a moving body, driving condition informationindicating driving details of the moving body, and position informationcorresponding to the captured image and the driving conditioninformation; analyzing a deterioration degree of an inspection targetappearing in the captured image; and computing a priority ranking of theinspection target based on deterioration degrees of the same inspectiontarget appearing in multiple captured images identified by the positioninformation, and the driving condition information corresponding to theidentified inspection target.

Advantageous Effects of Invention

According to the present invention, the accuracy of work for identifyinginspection targets and inspection locations that are of high prioritycan be improved.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating a summary of a deterioration diagnosissystem according to an embodiment of the present invention.

FIG. 2 is a hardware structure diagram of a deterioration diagnosisdevice according to an embodiment of the present invention.

FIG. 3 is a functional block diagram of a deterioration diagnosis deviceaccording to an embodiment of the present invention.

FIG. 4 is a diagram illustrating the hardware structure of a driverecorder according to an embodiment of the present invention.

FIG. 5 is a functional block diagram of a control device provided with adrive recorder according to an embodiment of the present invention.

FIG. 6 is a first diagram indicating the processing flow in a driverecorder according to an embodiment of the present invention.

FIG. 7 is a second diagram indicating the processing flow in a driverecorder according to an embodiment of the present invention.

FIG. 8 is a third diagram indicating the processing flow in a driverecorder according to an embodiment of the present invention.

FIG. 9 is a first diagram indicating the processing flow in adeterioration diagnosis device according to an embodiment of the presentinvention.

FIG. 10 is a second diagram indicating the processing flow in adeterioration diagnosis device according to an embodiment of the presentinvention.

FIG. 11 is a first diagram indicating an example of a display on aterminal device according to an embodiment of the present invention.

FIG. 12 is a second diagram indicating an example of a display on aterminal device according to an embodiment of the present invention.

FIG. 13 is a third diagram indicating an example of a display on aterminal device according to an embodiment of the present invention.

FIG. 14 is a fourth diagram indicating an example of a display on aterminal device according to an embodiment of the present invention.

FIG. 15 is a fifth diagram indicating an example of a display on aterminal device according to an embodiment of the present invention.

FIG. 16 is a sixth diagram indicating an example of a display on aterminal device according to an embodiment of the present invention.

FIG. 17 is a seventh diagram indicating an example of a display on aterminal device according to an embodiment of the present invention.

FIG. 18 is a diagram illustrating a second example of a deteriorationdiagnosis device according to an embodiment of the present invention.

FIG. 19 is a diagram illustrating the minimum structure of adeterioration diagnosis device according to an embodiment of the presentinvention.

EXAMPLE EMBODIMENT

Hereinafter, a deterioration diagnosis device according to an embodimentof the present invention will be explained with reference to thedrawings.

FIG. 1 is a diagram illustrating a summary of a deterioration diagnosissystem including the deterioration diagnosis device according to saidembodiment.

As illustrated in FIG. 1, the deterioration diagnosis system 100 isformed from a deterioration diagnosis device 1 and drive recorders 2that are connected by a wireless communication network or by a wiredcommunication network. The deterioration diagnosis device 1 is, forexample, a computer server (cloud server) that is installed by abusiness for performing deterioration diagnoses and that is connected tothe communication network. The drive recorders 2 are respectivelyprovided in a plurality of moving bodies. In FIG. 1, vehicles 20 areused as an example of moving bodies for explanation. The moving bodiesmay include autonomously driven vehicles. The drive recorders 2 havecameras and transmit, to the deterioration diagnosis device 1, capturedimages capturing the outsides of the vehicles 20.

FIG. 2 is a hardware structure diagram of a deterioration diagnosisdevice.

As illustrated in this drawing, the deterioration diagnosis device 1 isa computer provided with hardware such as a CPU (Central ProcessingUnit) 101, a ROM (Read-Only Memory) 102, a RAM (Random Access Memory)103, an HDD (Hard Disk Drive) 104, and a communication module 105.

FIG. 3 is a functional block diagram of a deterioration diagnosisdevice.

The deterioration diagnosis device 1 is activated when the power isswitched on, and a deterioration diagnosis program that is prestored inmemory or the like is executed by the CPU 101. As a result thereof, thedeterioration diagnosis device 1 is provided with the functions of acontrol unit 11, an acquisition unit 12, a deterioration degree analysisunit 14, a priority ranking computation unit 15, a diagnosis resultgeneration unit 16, and an output unit 17.

The control unit 11 controls the functional units of the deteriorationdiagnosis device 1.

The acquisition unit 12 acquires sensing information including at leastcaptured images captured by the drive recorders 2 mounted on thevehicles 20, driving condition information (driving condition data) ofthe vehicles 20, and position information for the positions at whichthose captured images and driving condition information were generated.The driving condition data will be described below.

The deterioration degree analysis unit 14 analyses the deteriorationdegrees of inspection targets appearing in the captured images. Theinspection targets in the present embodiment are public installationssuch as road surfaces and road-associated objects such as informationsigns and guard rails installed on the roadsides of roads. Deteriorationof road surfaces includes, for example, cracks and ruts in the roadsurfaces. Additionally, deterioration of road-associated objectsincludes, for example, changes in the shapes of signs and guard rails.

The priority ranking computation unit 15 computes priority rankings forinspection (examination) of different inspection targets appearing inmultiple captured images on the basis of the deterioration degrees ofthe same inspection target appearing in the captured images identifiedby the position information, and driving condition informationcorresponding to the inspection targets.

The diagnosis result generation unit 16 generates diagnosis resultsincluding the inspection priority rankings of public installations.

The output unit 17 outputs the diagnosis results to a designated device.The designated device is, for example, a computer used by personnel atan administrative authority that diagnoses the deterioration of publicinstallations, and performs inspections and maintenance thereon.

FIG. 4 is a diagram illustrating the hardware structure of a driverecorder.

The drive recorder 2 includes sensors 21, a communication device 22, acamera 23, a control device 24, a storage device 25, and the like. Inthe present embodiment, the sensor 21 includes multiple sensors. Thesensor 21 may include an acceleration sensor 211, a sound detectionsensor 212, a GPS sensor 213, and the like. These sensors 21 areinstalled at any position on the vehicle outside the drive recorder 2,and the drive recorder 2 may acquire information sensed by these sensors21. In the present embodiment, an example in which the sensors 21include an acceleration sensor 211, a sound detection sensor 212, and aGPS sensor 213 is explained, but the invention is not limited to thisexample. The sensor 21 need only include at least the GPS sensor 213.

The communication device 22 communicates and connects with thedeterioration diagnosis device 1. The camera 23 captures images of theoutside of the vehicle and generates at least one of moving images andstill images. The camera 23 may further capture images of the inside ofthe vehicle. However, in the present embodiment, the images that areused are images in which public installations appear, generated bycapturing images of the outside of the vehicle.

The control device 24 controls the functions of the drive recorder 2.The storage device 25 stores sensing information including at least oneof moving images and still images, and various types of informationsensed by the sensors 21.

The drive recorder 2 communicates and connects with the deteriorationdiagnosis device 1 via a base station or the like. The control device 24of the drive recorder 2 is a computer provided with a CPU, a ROM, a RAM,and the like.

FIG. 5 is a functional block diagram of the control device provided inthe drive recorder.

In the control device 24, when the drive recorder is activated, acontrol program stored in memory or the like is executed by the CPU. Asa result thereof, the control device 24 is provided with functionalunits including a vehicle information acquisition unit 241, a positioninformation acquisition unit 242, an acceleration informationacquisition unit 243, an event detection unit 244, an image generationunit 245, a driving condition data transmission unit 246, an event datatransmission unit 247, and an image transmission unit 248.

The vehicle information acquisition unit 241 acquires vehicleinformation including information (driver ID, vehicle type, vehicle ID)regarding the vehicle 20 that is stored in a memory unit inserted intothe drive recorder 2. The vehicle information that can be acquired bythe vehicle information acquisition unit 241 may further include, forexample, driving start time, driving stop time, vehicle speed over time,vehicle interior temperature, steering wheel angle, braking amount, andthe like.

The position information acquisition unit 242 acquires, from a GPSsensor 213 (FIG. 4) or the like, information including positioninformation (latitude information, longitude information) of the vehicleover time.

The acceleration information acquisition unit 243 acquires, from theacceleration sensor 211 (FIG. 4), acceleration information of thevehicle over time.

The event detection unit 244 determines, on the basis of theacceleration, whether or not the vehicle has been involved in certainevents. The certain events are, for example, hazardous events, and maybe events such as sudden acceleration or sudden deceleration.

The image generation unit 245 acquires image data including at least oneof moving images and still images from the camera 23 by capturing imageswith the camera 23, and generates and outputs, at designated intervals,captured images for uploading based on the image data. As an example,the image generation unit 245 generates captured images at a generationrate of 1 fps (frames per second). In other words, the image generationunit 245 generates one captured image per second. The image generationunit 245 generates captured images at a generation rate of 30 fps or thelike when an event is detected by the event detection unit 244.

The driving condition data transmission unit 246 transmits, to thedeterioration diagnosis device 1, driving condition data including theabove-mentioned vehicle information, position information (latitudeinformation, longitude information), and acceleration information, aswell as driving condition data generation times, the ID of the driverecorder 2, and the driver ID. The driving condition data may includeweather information such as the brightness outside the vehicle and theweather, and the travel time.

The event data transmission unit 247 transmits event data if theoccurrence of an event is detected by the event detection unit 244. Theevent data may include the acceleration, the speed, the steering wheelrotation angle, and the braking amount when the event occurrence wasdetected, the event occurrence time, vehicle position information(latitude information, longitude information), the ID of the driverecorder 2, the driver ID, and the like. The event data may furtherinclude other sensing information. The event data may include anidentifier indicating the type of event.

The image transmission unit 248 transmits the captured images generatedby the image generation unit 245 to the deterioration diagnosis device1.

FIG. 6 is a first diagram indicating the processing flow in a driverecorder.

Next, the processing flow in the drive recorder 2 will be explained inorder.

First, the driving condition data transmission process in the driverecorder 2 will be explained in accordance with FIG. 6.

When the electrical system in a vehicle is activated, the drive recorder2 starts operating (step S101). The sensors 21 in the drive recorder 2respectively start various types of sensing after the drive recorder 2has started (step S102). Additionally, the camera 23 starts capturingimages (step S103). Then, while the drive recorder 2 is operating, thevehicle information acquisition unit 241 in the control device 24acquires vehicle information (step S104).

Additionally, the position information acquisition unit 242 acquiresposition information (latitude information, longitude information) fromthe GPS sensor 213 at prescribed time intervals (step S105).Additionally, the acceleration information acquisition unit 243 acquiresacceleration information from the acceleration sensor 211 at prescribedtime intervals (step S106). The prescribed time intervals may, forexample, be every 0.1 seconds.

The driving condition data transmission unit 246 acquires the vehicleinformation, position information (latitude information, longitudeinformation), and acceleration information acquired in steps S104, S105,and S106, and generates driving condition data including the aboveinformation, the driving condition data generation time, the ID of thedrive recorder 2, and the driver ID (step S107).

The driving condition data transmission unit 246 requests thecommunication device 22 to transmit the driving condition data to thedeterioration diagnosis device 1. The communication device 22 transmitsthe driving condition data to the deterioration diagnosis device 1 (stepS108). The control device 24 determines whether or not the processshould be ended (step S109), and repeats the process from step S102until the process ends.

FIG. 7 is a second diagram indicating the processing flow in the driverecorder.

The drive recorder 2 performs an event detection process in parallelwith the driving condition data transmission process.

First, when the drive recorder 2 is started, the event detection unit244 in the control device 24 acquires acceleration information from theacceleration information acquisition unit 243 at prescribed timeintervals (step S201). Additionally, the event detection unit 244acquires speed information from the vehicle information acquisition unit241 at prescribed time intervals (step S202). The event detection unit244 detects whether or not a vehicle has been involved in an event basedon the change over time in the acceleration and speed of the vehicle(step S203).

The event detection unit 244 may detect whether or not an event hasoccurred by using information such as the steering wheel angle or thebraking amount acquired, as vehicle information, by the vehicleinformation acquisition unit 241.

For example, the steering wheel rotating by a prescribed angle or morein a short time period such as 0.1 seconds indicates a steering wheelaction for the case in which the driver has suddenly turned the steeringwheel by a large amount. For this reason, the event detection unit 244detects the occurrence of an event such as hazard avoidance.Additionally, the braking amount being a prescribed braking amount ormore indicates a brake action for the case in which the driver hassuddenly braked. For this reason, the event detection unit 244 detectsthe occurrence of an event such as hazard avoidance. In this case, theevent data is hazard information. The event data (hazard information)may include information (braking amount, steering wheel angle,acceleration information, and acceleration information due to acollision in the case of a collision accident) acquired by the varioussensors when an accident has occurred.

When the occurrence of an event is detected, the event detection unit244 generates event data (step S204). The event data may include theacceleration, the speed, the steering wheel rotation angle, and thebraking amount, the event occurrence time, vehicle position information(latitude information, longitude information), the ID of the driverecorder 2, the driver ID, and the like, at the time the eventoccurrence was detected. The event data may further include othersensing information.

The event data transmission unit 247 acquires the event data from theevent detection unit 244. The event data transmission unit 247 instructsthe communication device 22 to transmit the event data to thedeterioration diagnosis device 1. The communication device 22 transmitsthe event data to the deterioration diagnosis device 1 (step S205). Thecontrol device 24 determines whether or not the process should be ended(step S206), and repeats the process from step S201 until the processends.

The events mentioned above are events indicating hazardous situations inthe present embodiment, and the event data is one embodiment ofinformation indicating driving conditions. Therefore, the event data maybe included in the driving condition data and may be sent to thedeterioration diagnosis device 1 by the driving condition datatransmission unit 246.

FIG. 8 is a third diagram indicating the processing flow in the driverecorder.

Next, the process by which the drive recorder 2 transmits capturedimages to the deterioration diagnosis device 1 will be explained inaccordance with FIG. 8.

The image generation unit 245 acquires, from the camera 23, image dataincluding at least one of moving images and still images captured by thecamera 23 (step S301). The image generation unit 145 generates, atprescribed intervals, captured images for uploading, based on theacquired image data (step S302). Additionally, the image generation unit145 may generate captured images at prescribed intervals by acquiringimage data from the camera 23.

The image generation unit 145 instructs the image transmission unit 248to transmit the captured images that have been generated. The imagegeneration unit 145 may include, as attribute information in dataindicated by the captured images, information such as the generationtime (or image capture time), vehicle position information (latitudeinformation, longitude information), the ID of the drive recorder 2, andthe driver ID. The image transmission unit 248 transmits the capturedimage to the deterioration diagnosis device 1 by means of thecommunication device 22 (step S303).

FIG. 9 is a first diagram indicating the processing flow in thedeterioration diagnosis device.

In the deterioration diagnosis device 1, the acquisition unit 12acquires, through means of the communication module 105, drivingcondition data transmitted from the communication device 22 of a vehicle20 (step S401). Additionally, the acquisition unit 12 acquires eventdata transmitted from the communication device 22 in the vehicle 20through the communication module 105 (step S402). Additionally, theacquisition unit 12 acquires captured images transmitted from thecommunication device 22 in the vehicle 20 (step S403).

The acquisition unit 12 outputs the driving condition data, the eventdata, and the captured images acquired in steps S401 to S403 to thedeterioration degree analysis unit 14.

The deterioration degree analysis unit 14, based on the time informationand the ID of the drive recorder 2 included in the driving conditiondata, the event data, and the captured image, identifies thecorrespondence relationship between the information generated at timesthat can be estimated to be the same time (step S404). For example, thedeterioration degree analysis unit 14 links driving condition data,event data, and captured images for which the time information isincluded within the same time band.

The deterioration degree analysis unit 14, upon identifying thecorrespondence relationship between the driving condition data, theevent data, and the captured images, temporarily records sensinginformation, organized by the correspondence relationship, so as to bearranged in the order of time (step S405). For example, thedeterioration degree analysis unit 14 records sensing information in thesame time band in chronological order. The sensing information isinformation included in the driving condition data, the event data, andthe captured images.

There may be cases in which the sensing information does not includeevent data, such as cases in which an event is not detected at the timedriving condition data or captured images were generated. Sensinginformation in which driving condition data, event data, and capturedimages are organized may be generated at prescribed intervals, such asevery second, by the drive recorder 2. In such cases, the drive recorder2 may periodically transmit the sensing information to the deteriorationdiagnosis device 1.

Of the sensing information that has been temporarily recorded, thedeterioration degree analysis unit 14 discards sensing informationcontaining captured images not suitable for image diagnosis (step S406).

For example, the deterioration degree analysis unit 14 acquires, fromthe driving condition data included in the sensing information,information such as the weather conditions in the area in which thevehicle 20 is traveling, the travel time, and the like, as well as thetravel speed of the vehicle 20. In the deterioration degree analysisunit 14, if the information indicated by the weather conditions indicaterain or if the time is nighttime, then there is a possibility that theappearance of the inspection target has not been accurately captured.For this reason, sensing information including such weather conditioninformation in the driving condition data is discarded. Additionally, inthe deterioration degree analysis unit 14, if the travel speed of thevehicle is a prescribed speed or higher, then there is a possibilitythat the appearance of the inspection target has not been accuratelycaptured. For this reason, sensing information including such a travelspeed in the driving condition data is discarded.

This discarding process is an example of a process for the deteriorationdegree analysis unit 14 to identify captured images matching prescribedsensing conditions based on the information contained in the sensinginformation and analyzing the deterioration degrees of inspectiontargets appearing in such captured images. The prescribed sensingconditions are capture conditions of captured images indicating that theimages are suitable for image analysis. The categories of the captureconditions are, for example, the weather conditions, the travel time,the travel speed, and the like.

Next, the deterioration degree analysis unit 14 acquires sensinginformation including the oldest time information of the temporarilystored unprocessed sensing information (step S407). Furthermore, thedeterioration degree analysis unit 14 acquires the captured images inthe sensing information. The deterioration degree analysis unit 14determines whether or not an inspection target is included in thecaptured images (step S408). Inspection targets are road surfaces,vehicle traffic lines printed on road surfaces, guard rails, signs,traffic lights, streetlamps, and the like.

The deterioration degree analysis unit 14 uses image pattern matching,machine learning processes, or AI (Artificial Intelligence) analyses todetermine whether or not these inspection targets are contained in thecaptured images. Known technology may be used to recognize theinspection targets in the captured images.

If the deterioration degree analysis unit 14 determines that aninspection target is included in a captured image (YES in step S408),then an inspection target ID identifying the type of the inspectiontarget is inserted in sensing information. The inspection target ID issuch that, even if the inspection target is of the same type, adifferent ID is assigned for each deterioration type of the inspectiontarget. In other words, the inspection target ID is also identificationinformation indicating the inspection target type.

The deterioration degree analysis unit 14 computes the deteriorationdegree of the inspection target on the basis of the captured image inwhich the inspection target is recognized (step S409). When thedeterioration degree of the inspection target is computed, thedeterioration degree analysis unit 14 inserts the deterioration degreevalue in the sensing information. The deterioration degree analysis unit14 records the sensing information including the inspection target IDand the deterioration degree value, as a first diagnosis result, in astorage unit such as a database (step S410). The deterioration degreeanalysis unit 14 repeatedly performs the processes in step S407 to stepS410 on unprocessed sensing information.

A specific example of the deterioration degree computation process(S409) will be explained.

As mentioned above, the deterioration degree analysis unit 14 uses imagepattern matching, machine learning processes, AI analysis, or the liketo identify the type of the inspection target appearing in the capturedimage. Types of inspection targets are, for example, cracks, potholes,and ruts in road surfaces, vehicle traffic lines, guard rails, signs,traffic lights, streetlamps, and the like.

In the case in which the inspection target appearing in the capturedimage is a crack in a road surface, the deterioration degree analysisunit 14 identifies the size (length, width) of the crack from thecaptured image and computes the deterioration degree based on the sizeof the crack. The larger the size of the crack, the higher thedeterioration degree is.

Additionally, in the case in which the inspection target appearing inthe captured image is a hole in a road surface, the deterioration degreeanalysis unit 14 identifies the size (diameter, width) of the hole fromthe captured image and computes the deterioration degree based on thesize of the hole. The larger the size of the hole, the higher thedeterioration degree is.

Additionally, in the case in which the inspection target appearing inthe captured image is a road surface, the deterioration degree analysisunit 14 identifies the size (length, width) of the rut from the capturedimage and computes the deterioration degree based on the size of therut. The larger the size of the rut, the higher the deterioration degreeis.

Additionally, in the case in which the inspection target appearing inthe captured image is a vehicle traffic line that is printed on a roadsurface, the deterioration degree analysis unit 14 identifies the degreeof distinctness (degree of whiteness of white lines, color value) of thetraffic line from the captured image and computes the deteriorationdegree based on the degree of distinctness. The lower the degree ofdistinctness, the higher the deterioration degree is.

Additionally, in the case in which the inspection target appearing inthe captured image is a guard rail installed on a roadside, thedeterioration degree analysis unit 14 identifies the shape of the guardrail from the captured image and computes the deterioration degree basedon the difference between that shape and a prescribed shape. The largerthe difference between the shape of the guard rail and the prescribedshape, the higher the deterioration degree is.

Additionally, in the case in which the inspection target appearing inthe captured image is a sign installed on a roadside, the deteriorationdegree analysis unit 14 identifies the shape (degree of pole bending,orientation of the sign itself, etc.) of the sign from the capturedimage and computes the deterioration degree based on the differencebetween that shape and a prescribed shape. The larger the differencebetween the shape of the sign and the prescribed shape, the higher thedeterioration degree is.

Additionally, in the case in which the inspection target appearing inthe captured image is a traffic light installed over a road, thedeterioration degree analysis unit 14 identifies the state (signal lightbrightness, etc.) of the traffic light from the captured image andcomputes the deterioration degree based on that state. The lower thesignal light brightness of the traffic light, the higher thedeterioration degree is.

Additionally, in the case in which the inspection target appearing inthe captured image is a streetlamp installed on a roadside, thedeterioration degree analysis unit 14 identifies the state (lightbrightness, pole tilt, etc.) of the streetlamp from the captured imageand computes the deterioration degree based on that state. The lower thelight brightness of the streetlamp, or the greater the tilt of the pole,the higher the deterioration degree is.

The first diagnosis results (S410) mentioned above include recognitionresults for inspection targets in captured images obtained by usingsensing information acquired from multiple different vehicles 20, anddeterioration degree estimation results for those inspection targets. Inother words, the stored first diagnosis results include deteriorationdegrees based on sensing information acquired by each of multiplevehicles 20. Therefore, the deterioration degree analysis unit 14computes a statistical value of the deterioration degree of the sameinspection target acquired by different vehicles 20 included in thefirst analysis results. In other words, the deterioration degreeanalysis unit 14 extracts multiple first diagnosis results for the sameinspection target from among the stored first diagnosis results, andcomputes a statistical value based on the multiple first diagnosisresults that have been extracted.

Specifically, the deterioration degree analysis unit 14 acquires, assecond diagnosis targets, multiple sets of sensing information includingcaptured images of the same inspection target captured within aprescribed time period, based on the position information (latitudeinformation, longitude information), the inspection target IDs, and thetimes included in the sensing information (step S411).

For example, the deterioration degree analysis unit 14 identifies thesame inspection targets based on the position information and theinspection target IDs. For example, the deterioration degree analysisunit 14 identifies inspection targets having proximate positioninformation and matching inspection target IDs as the same inspectiontarget. The deterioration degree analysis unit 14 acquires, as seconddiagnosis targets, from multiple sets of sensing information includingcaptured images of the same inspection target that has been identified,sensing information including captured images of the same inspectiontarget captured within a prescribed time period.

If there is only one set of sensing information including capturedimages for the same inspection target captured within a prescribed timeperiod, then that sensing information may be acquired as a seconddiagnosis target. The prescribed time period may, for example, be oneday, one week, one month, or the like.

The deterioration degree analysis unit 14 statistically processes thedeterioration degrees included in one or multiple sets of sensinginformation acquired as second diagnosis targets, and computes a singledeterioration degree for the specific inspection target indicated bythose sets of sensing information (step S412). Specifically, thedeterioration degree analysis unit 14 may compute an average value ofthe deterioration degree values included in one or multiple sets ofsensing information acquired as second diagnosis targets, and may takethat average value as a single deterioration degree for the specificinspection target indicated by those sets of sensing information.

Alternatively, the deterioration degree analysis unit 14 classifies thedeterioration degree values included in one or multiple sets of sensinginformation acquired as second diagnosis targets as being either equalto or higher than a prescribed threshold value, or less than thethreshold value. In this case, the deterioration degree analysis unit 14may compute a deterioration degree that indicates deterioration whenthere is less than a prescribed proportion of the sensing informationwhose a deterioration degree lower than the threshold value, and thatindicates that it has not deteriorated when there is more than or equalto the prescribed proportion of the sensing information whose thedeterioration degree lower than the threshold value. In other words, thedeterioration degree analysis unit 14 determines that an inspectiontarget is not deteriorated when the deterioration degree is lower thanthe threshold value in the prescribed proportion or more of the multiplesets of sensing information of the second diagnosis targets.

For example, there may be cases in which there are, for the sameinspection target, sensing information in which a deterioration degreeindicating a crack in the road surface is computed due to the influenceof shadows, obstacles, or the like, and sensing information including adeterioration degree having a low value not indicating a crack. If thereis at least a prescribed proportion of sensing information including adeterioration degree not indicating a crack in the road surface, then itcan be determined that the sensing information includes cases in which acrack in the road surface has been erroneously identified due to theinfluence of shadows. For this reason, the deterioration degree analysisunit 14 can compute a deterioration degree indicating that there is nodeterioration.

In other words, there are cases in which the multiple sets of sensinginformation in the second diagnosis targets include sensing informationthat has been erroneously determined to have a deterioration degree thatis equal to or higher than the threshold value due to the influence ofthe image capture conditions. Even in such cases, an appropriatedeterioration degree can be computed by basing the computation onmultiple sets of sensing information.

The deterioration degree analysis unit 14 may compute, as a statisticalvalue of the deterioration degrees, a deterioration degree that exhibitsa high value in the case in which the deterioration degree is increasingor in the case in which the rate of increase in the deterioration degreeis fast, based on the change over time in the deterioration degreesincluded in multiple sets of sensing information acquired as the seconddiagnosis targets. In other words, the deterioration degree analysisunit 14 may compute a high deterioration degree in the case in which thedeterioration degrees in multiple sets of sensing information areincreasing with the passage of time, or in the case in which the rate ofincrease in the deterioration degree is high.

The deterioration degree analysis unit 14 records, as second diagnosisresults, sensing information including the deterioration degreesobtained after statistical processing computed by using the sensinginformation acquired as the second diagnosis targets (step S413). Thedeterioration degree analysis unit 14 determines whether or not thesensing information included in the first diagnosis results includesunprocessed information (step S414). If there is unprocessed information(YES in S414), then the deterioration degree analysis unit 14 repeatsthe process from step S411.

Next, the priority ranking computation unit 15 computes the inspection(examination) priority level of an inspection target based on thedeterioration degrees of the same inspection target and the drivingconditions corresponding to the inspection target (step S415).

Specifically, the priority ranking computation unit 15 acquires one setof sensing information among the sensing information recorded as seconddiagnosis results. The priority ranking computation unit 15 acquires theinspection target ID included in the sensing information. The inspectiontarget ID is information identifying the type of the inspection target.

In order to determine whether or not the deterioration of the inspectiontarget indicated by this inspection target ID has affected driving, itis preferable to analyze the event data included in the sensinginformation. For example, when the inspection target ID indicates a holein the road surface, there are cases in which the event data includesinformation indicating that the driver has suddenly turned the steeringwheel or that the driver has braked during the operation of the vehicle20.

Therefore, the priority ranking computation unit 15 determines, on thebasis of the event data included in the sensing information, whether ornot an event possibly corresponding to the inspection target ID includedin the sensing information has occurred.

The priority ranking computation unit 15 computes the examinationpriority level based on the deterioration degree included in the sensinginformation acquired from the second diagnosis results, and whether ornot that sensing information includes event data indicating an eventcorresponding to the inspection target ID.

If event data indicating an event corresponding to the inspection targetID is included in sensing information in the second diagnosis results,then it can be understood that there is a high likelihood that a drivingoperation due to which the event occurred was caused by thedeterioration in the inspection target. Therefore, if event dataindicating an event corresponding to the inspection target ID isincluded in sensing information, then the priority ranking computationunit 15, for example, computes the priority level by multiplying thedeterioration degree by a prescribed weighting coefficient.

Priority level values corresponding to the inspection target ID andpriority level values corresponding to the deterioration degrees may bepredefined. The priority ranking computation unit 15 reads inspectiontarget IDs, deterioration degrees, and event data from the sensinginformation. The priority ranking computation unit 15 computes prioritylevels, for example, by multiplying all of a priority level valuecorresponding to an inspection target ID, a priority level valuecorresponding to a deterioration degree, and a weighting coefficientcorresponding to event data indicating an event corresponding to theinspection target ID. This manner of computation of the priority levelis one example, and the priority level may be computed by anothermethod.

The priority ranking computation unit 15 records priority rankingcomputation results linking the sensing information acquired from thesecond diagnosis results with a priority level computed for that sensinginformation (step S416).

The deterioration diagnosis device 1 may accept the registration ofreport information regarding an inspection target and may further usethat report information to compute the priority level. The reportinformation is, for example, inspection target deterioration informationreported by a resident. For example, report information includingposition information (latitude information, longitude information) ofinspection targets and information regarding the deterioration degreesthereof from residents is registered and stored in a database or thelike. Furthermore, when computing the priority levels, the priorityranking computation unit 15 reads, from the database, report informationcorresponding to the position information included in the sensinginformation in the second diagnosis results. The priority rankingcomputation unit 15 may compute the priority level by furthermultiplying a weighting coefficient corresponding to the value of thedeterioration degree included in the report information.

Additionally, the priority ranking computation unit 15 may compute apriority level based on the number of complaints from residents, thedistances from public facilities (schools, hospitals, etc.), thepopulation density, and the like regarding each inspection target. Thepriority ranking computation unit 15 computes the priority level byfurther multiplying a large weighting coefficient in the case in whichthere are many complaints, in the case in which the distance from apublic facility is close, and in the case in which the populationdensity is high.

According to the above processes, the priority ranking computation unit15 computes examination priority rankings of respective inspectiontargets based on at least the deterioration degrees of the sameinspection target appearing in multiple captured images, and drivingcondition data (event data) and report information corresponding to theinspection targets. Therefore, in cases in which hazard avoidance eventsof vehicles due to the deterioration conditions of inspection targetshave occurred, or in cases in which there have been reports, weightingcoefficients can be multiplied to compute higher-value priority levels.Therefore, more highly accurate examination priority rankings of theinspection targets can be specified in accordance with the drivingconditions. Thus, the accuracy of the work of identifying high-priorityinspection targets and high-priority inspection locations can beincreased.

Additionally, according to the process mentioned above, the priorityranking is computed by using sensing information including capturedimages captured by drive recorders 2 mounted on vehicles 20. For thisreason, the priority ranking for examining public installations can beautomatically computed simply by having personnel drive vehicles 20through town. As a result thereof, the labor required for the work ofidentifying high-priority inspection targets and inspection locationscan be reduced.

In the description above, the case in which the drive recorders 2 aremounted on vehicles 20 was used as an example, but the drive recorders 2may also be mounted on other moving bodies. The other moving bodies may,for example, be bicycles, mobility scooters, or the like. Additionally,in the description above, the case in which the inspection targets areroad-associated installations provided on road surfaces and roadsides isused as an example. However, the inspection targets may, for example, berailroads or runways, or may be various installations provided in astation, an airport, a building, or the like. In that case, a driverecorder 2 may be mounted on a mobile patrol device and be moved insidea facility such as a station, an airport, or a building, and mayidentify the examination priority rankings of various installations bymeans of a similar process.

FIG. 10 is a second diagram indicating the processing flow in thedeterioration diagnosis device.

Next, the processing in the diagnosis result generation unit 16 in thedeterioration diagnosis device 1 will be explained.

According to the process described above, priority levels are insertedin the sets of sensing information acquired as second diagnosis targets,and the sets of sensing information are recorded in the database aspriority ranking computation results. In such a state, the diagnosisresult generation unit 16 reads the priority levels of the sets ofsensing information recorded as priority ranking computation results(step S501). The diagnosis result generation unit 16 identifies sets ofsensing information including high priority levels of a prescribed valueor higher (step S502).

The diagnosis result generation unit 16 acquires map information for adesignated area (step S503). The designated area may be predetermined,or may be input by a user via an interface. The diagnosis resultgeneration unit 16, for example, based on the number of sets of sensinginformation having position information included in sub-areas obtainedby horizontally and vertically dividing the designated area indicated bythe map information, computes the priority level of each sub-area (stepS504). For example, in accordance with the number of sets of sensinginformation, each sub-area is assigned information indicating a highpriority level, a medium priority level, or a low priority level. Thediagnosis result generation unit 16 generates output map information inwhich, among the sub-areas included in the designated area of the mapinformation, the sub-areas having a high priority level are displayed ina highlighted manner (step S505). The diagnosis result generation unit16 links the generated output map information with area IDs and recordsthe information in the database.

The diagnosis result generation unit 16 may similarly generate outputmap information for the map information of multiple areas. The diagnosisresult generation unit 16 may compute the priority levels of thesub-areas by using the priority levels included in the sensinginformation in addition to the number of sets of sensing informationhaving position information contained in the sub-areas. As one example,the diagnosis result generation unit 16 acquires the examinationpriority levels of inspection targets computed in step S415 regardingsets of sensing information having position information contained in asub-area. The diagnosis result generation unit 16 computes the prioritylevel of the sub-area by using a computational formula defined so thatsub-areas in which high-priority inspection targets are present areassigned high priority levels.

The deterioration diagnosis device 1 receives diagnosis result outputrequests from prescribed devices (terminal devices) (step S506). Theoutput unit of the deterioration diagnosis device 1 can generatediagnosis result screen information based on the diagnosis result outputrequest, and output the information to the terminal device. For example,the deterioration diagnosis device 1 identifies a user ID included inthe diagnosis result output request (step S507). It is assumed that anarea ID corresponding to the user ID was predesignated when the userregistered for the service, and is stored in a user database or thelike. The output unit 17 acquires the area ID corresponding to the userID from the user database (step S508).

The output unit 17 acquires output map information recorded in thedatabase so as to be linked to the area ID, and acquires, from thepriority ranking computation results, sets of sensing informationholding position information included in the area range indicated bythat area ID (step S509). Alternatively, the output unit 17 may storeinformation regarding the area range predesignated by the user.

The output unit 17 generates diagnosis result screen information basedon the acquired output map information and the sensing information (stepS510). The output unit 17 transmits the diagnosis result screeninformation to a terminal device (step S511). As a result thereof, thediagnosis result screen information is displayed on the terminal device.

FIG. 11 to FIG. 17 are diagrams illustrating examples of displays on theterminal device.

FIG. 11 is an example of a display of a menu screen. For example,personnel (user) at an administrative authority that performsmaintenance of public installations activates the menu screen and startswork for checking the deterioration state of public installations.

FIG. 12 to FIG. 17 are diagrams illustrating examples of displays ofdiagnosis result (analysis result) screen information. In FIG. 12, thesub-areas obtained by horizontally and vertically dividing the areaindicated by designated map information are displayed so as to becolor-coded in accordance with the probability of deterioration.Additionally, in FIG. 12, the results of computation of the probabilityof deterioration using, as indices, AI analysis results regarding cracksand ruts, the comments of residents (report information), and near-missinformation (hazard information) are displayed.

In FIG. 13, a sub-area selected by the user in the display exampleillustrated in FIG. 12 is enlarged.

In FIG. 14, AI analysis results regarding cracks and ruts, the commentsof residents, and near-miss information are selected as indices, andcorresponding icons are displayed on the map at positions where adeterioration site may exist.

FIG. 15 is an example of the display of an image of a deteriorationsite, the level of deterioration, and the change in the deteriorationover time. FIG. 15 illustrates an example of a display of the change inthe deterioration over time by a moving image.

FIG. 16 is an example of the display of the comments (reportinformation) of residents.

FIG. 17 is an example of the display of near-miss information (hazardinformation).

The monitor of the terminal device is, for example, composed of a touchpanel or the like, and prescribed positions on the screen of the monitorcan be selected. The user selects a prescribed sub-area from the mapinformation displayed in a display region of the diagnosis result screeninformation displayed on the monitor of the terminal device (FIG. 12).The terminal device transmits an ID of the sub-area to the deteriorationdiagnosis device 1.

The output unit 17 acquires, from the database, data for an enlarged mapof the area indicated by the sub-area ID and sensing informationincluding position information included in the area of that map, andtransmits the data to the terminal device. The terminal device displays,in the display region, the enlarged map of the sub-area received fromthe deterioration diagnosis device 1 (FIG. 13). The terminal devicedisplays icon images corresponding to the inspection targets locatedwithin the sub-area in the enlarged map at locations on the enlarged mapcorresponding to the latitudes and longitudes of the inspection targets.The user can select icon images corresponding to the inspection targetsdisplayed on the enlarged map.

When the user selects a certain icon image on the enlarged map, theterminal device detects the location of that icon image. The terminaldevice identifies sensing information including the inspection targetcorresponding to that icon image. The terminal device acquiresinformation such as captured images, the degree of deterioration, reportinformation, and the inspection target ID included in that sensinginformation, and outputs the information to a prescribed display regionin the diagnosis result screen information (FIG. 14).

If the residents' comments icon is further selected, then the terminaldevice may display stored report information, as illustrated in FIG. 16.Additionally, when the near-miss icon is selected, the terminal devicemay display a graph of hazard information and analysis results, asillustrated in FIG. 17.

The output unit 17 of the deterioration diagnosis device 1 may generatea moving image generated by using multiple captured images of the sameinspection target and joining them in chronological order for beingdisplayed in the display region of the diagnosis result screeninformation (FIG. 15).

The output unit 17 of the deterioration diagnosis device 1 identifiessensing information including the same position and the same inspectiontarget ID in a prescribed time period, and acquires the captured imagesincluded in those sets of sensing information. The output unit 17generates a moving image by joining the captured images in the order ofthe generation times included in the sensing information.

Upon detecting that an icon image has been selected on the enlarged mapdisplayed in the display region, the output unit 17 may transmit, to theterminal device, a moving image generated for the position and theinspection target corresponding to that icon image. The terminal devicedisplays the moving image in the display region and plays the movingimage based on user operations. As a result thereof, the user can viewthe moving image displayed and generated in the display region andobserve the change in the deterioration degree of the inspection targetover time.

In the description above, the deterioration diagnosis device 1 computesthe deterioration degree in step S409. However, the deteriorationdiagnosis device 1 may be configured so as to determine whether or notan inspection target is included in step S408 without computing thedeterioration degree, and to record sensing information including onlythe determination result as the first diagnosis result in step S410. Inthis case, the deterioration diagnosis device 1 may be configured so asnot to perform the process in steps 411 to S416 and to generate andtransmit, to the terminal device, diagnosis result screen informationindicating where the inspection target is located based on the sensinginformation including whether or not there is an inspection target. Inthis case, the deterioration diagnosis device 1 may output diagnosisresult screen information for the range of a sub-area matching theconditions of position information designated on the basis of thegeographical range for which the user is responsible.

FIG. 18 is a diagram illustrating a second embodiment of thedeterioration diagnosis device.

The deterioration diagnosis device 1 illustrated in FIG. 18 is a diagramillustrating an example for the case in which each function in thedeterioration diagnosis device is provided inside a drive recorder 2.

By executing a program, a control device 24 in the drive recorder 2 mayprovide, in addition to the functions in the control device 24illustrated in FIG. 5, the functions of the deterioration degreeanalysis unit 14, the priority ranking computation unit 15, thediagnosis result generation unit 16, and the output unit 17 in thedeterioration diagnosis device 1 illustrated in FIG. 3. The processingin the respective functional units in this case is similar to thatmentioned above. However, in this case, the drive recorder 2 receivesthe sensing information from other vehicles via a server or the likethrough a communication network.

FIG. 19 is a diagram illustrating the minimum structure of thedeterioration diagnosis device.

The deterioration diagnosis device 1 illustrated in this drawing isprovided with at least an acquisition unit 12, a deterioration degreeanalysis unit 14, and a priority ranking computation unit 15.

The acquisition unit 12 acquires sensing information including at leastcaptured images captured by an image capture device mounted on a movingbody, driving condition information of the moving body, and positioninformation for the positions at which those capture images and drivingcondition information were generated.

The deterioration degree analysis unit 14 analyses the deteriorationdegrees of inspection targets appearing in the captured images.

The priority ranking computation unit 15 computes the priority rankingsof different inspection targets appearing in multiple captured images onthe basis of the deterioration degrees of the same inspection targetappearing in the captured images identified by the position information,and driving condition information corresponding to the inspectiontargets.

The priority ranking computation unit may be provided in a device thatis separate from the deterioration diagnosis device. Thus, thedeterioration diagnosis system overall may be in a form including theacquisition unit, the deterioration degree analysis unit, and thepriority ranking computation unit.

The above-mentioned deterioration diagnosis device 1, the control device24 in the drive recorder 2, and the terminal device have computersystems in the interiors thereof. Furthermore, the steps in theabove-mentioned processes are stored in the form of a program in acomputer-readable recording medium, and the above-mentioned processesare performed by a computer reading and executing this program.

The above-mentioned program may be for realizing just a portion of theafore-mentioned functions. Furthermore, it may be a so-called differencefile (difference program) that can realize the aforementioned functionsby being combined with a program that is already recorded in a computersystem.

Priority is claimed on Japanese Patent Application No. 2018-139617,filed Jul. 25, 2018, the entire disclosure of which is incorporatedherein by reference.

INDUSTRIAL APPLICABILITY

According to the present invention, the accuracy of work for identifyinginspection targets and inspection locations that are of high prioritycan be improved.

REFERENCE SIGNS LIST

-   1 Deterioration diagnosis deice-   2 Drive recorder-   11 Control unit-   12 Acquisition unit-   14 Deterioration degree analysis unit-   15 Priority ranking computation unit-   16 Diagnosis result generation unit-   17 Output unit-   21 Sensor-   22 Communication device-   23 Camera-   24 Control device-   25 Storage device-   211 Acceleration sensor-   212 Sound detection sensor-   213 GPS sensor-   241 Vehicle information acquisition unit-   242 Position information acquisition unit-   243 Acceleration information acquisition unit-   244 Event detection unit-   245 Image generation unit-   246 Driving condition data transmission unit-   247 Event data transmission unit-   248 Image transmission unit

1. A deterioration diagnosis device comprising: at least one memoryconfigured to store instructions; and at least one processor configuredto execute the instructions to; acquire sensing information including atleast a captured image captured by an image capture device mounted on amoving body, driving condition information indicating driving details ofthe moving body, and position information corresponding to the capturedimage and the driving condition information; analyze a deteriorationdegree of an inspection target appearing in the captured image; andcompute a priority ranking of the inspection target based ondeterioration degrees of the same inspection target appearing inmultiple captured images identified by the position information, and thedriving condition information corresponding to the identified inspectiontarget.
 2. The deterioration diagnosis device according to claim 1,wherein the at least one processor is configured to execute theinstructions to: identify at least hazard information which is one typeof the driving condition information corresponding to the identifiedinspection target, and compute an inspection priority level of theinspection target as the priority ranking based on the analyzeddeterioration degree and a weighting coefficient corresponding to thehazard information.
 3. The deterioration diagnosis device according toclaim 1, wherein the at least one processor is configured to execute theinstructions to: compute the priority ranking of the inspection targetbased on deterioration report information regarding the inspectiontarget.
 4. The deterioration diagnosis device according to claim 1,wherein the at least one processor is configured to execute theinstructions to: identify the captured image that matches prescribedsensing conditions based on the information contained in the sensinginformation, and analyze the deterioration degree of the inspectiontarget appearing in the captured image.
 5. The deterioration diagnosisdevice according to claim 1, wherein the at least one processor isconfigured to execute the instructions to: compute the deteriorationdegree of the same inspection target appearing in the multiple capturedimages based on statistical results regarding the deterioration degreesof the same inspection target appearing in the multiple captured images.6. The deterioration diagnosis device according to claim 1, wherein theat least one processor is further configured to execute the instructionsto: output map information indicating an area having a high priorityranking identified based on a number of the inspection targets in whichthe deterioration degree is equal to or higher than a threshold value,and the priority rankings of the inspection targets.
 7. A deteriorationdiagnosis system comprising: an image capture device that captures animage of the outside of a moving body; at least one memory configured tostore instructions; and at least one processor configured to execute theinstructions to; acquire sensing information including at least acaptured image captured by the image capture device, driving conditioninformation indicating driving details of the moving body, and positioninformation corresponding to the captured image and the drivingcondition information; analyze a deterioration degree of an inspectiontarget appearing in the captured image; and compute a priority rankingof the inspection target based on deterioration degrees of the sameinspection target appearing in multiple captured images identified bythe position information, and the driving condition informationcorresponding to the identified inspection target.
 8. A deteriorationdiagnosis method comprising: acquiring sensing information including atleast a captured image captured by an image capture device mounted on amoving body, driving condition information indicating driving details ofthe moving body, and position information corresponding to the capturedimage and the driving condition information; analyzing a deteriorationdegree of an inspection target appearing in the captured image; andcomputing a priority ranking of the inspection target based ondeterioration degrees of the same inspection target appearing inmultiple captured images identified by the position information, and thedriving condition information corresponding to the identified inspectiontarget.